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      name:  <unnamed>
       log:  ...\1805 Roadmaps\Table A8 any info demo.log
  log type:  text
 opened on:   8 May 2018, 15:35:40

. 
. set seed Xf31a93d5f6d3127fe027dfd2b01988e4000420dd

. 
. 
. probit vote_leemar ///
>      all_info all_info_D1 ///
>      d1 female inc_level pol_interest2 ret_level2

Iteration 0:   log likelihood = -224.82248  
Iteration 1:   log likelihood = -188.19271  
Iteration 2:   log likelihood = -188.13675  
Iteration 3:   log likelihood = -188.13674  

Probit regression                                 Number of obs   =        333
                                                  LR chi2(7)      =      73.37
                                                  Prob > chi2     =     0.0000
Log likelihood = -188.13674                       Pseudo R2       =     0.1632

-------------------------------------------------------------------------------
  vote_leemar |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     all_info |  -.1379901   .2359029    -0.58   0.559    -.6003514    .3243712
  all_info_D1 |   .2839148   .2769573     1.03   0.305    -.2589115    .8267412
           d1 |   .4122563   .2500171     1.65   0.099    -.0777683    .9022809
       female |  -.3562941   .1510436    -2.36   0.018    -.6523341   -.0602542
    inc_level |   .1077107   .0406876     2.65   0.008     .0279645    .1874569
pol_interest2 |   .1007426   .0970938     1.04   0.299    -.0895577    .2910428
   ret_level2 |  -.2508242   .1023352    -2.45   0.014    -.4513974   -.0502509
        _cons |  -.3923403   .4657305    -0.84   0.400    -1.305155    .5204747
-------------------------------------------------------------------------------

. 
. tabstat d1 if e(sample), s(n mean sd min p25 p50 p75 max)

    variable |         N      mean        sd       min       p25       p50       p75       max
-------------+--------------------------------------------------------------------------------
          d1 |       333  .4704775  .7404071    -1.848      .017      .448     1.021     1.978
----------------------------------------------------------------------------------------------

. tabstat inc_level if e(sample), s(n mean sd min p25 p50 p75 max)

    variable |         N      mean        sd       min       p25       p50       p75       max
-------------+--------------------------------------------------------------------------------
   inc_level |       333  3.636637  1.903574         1         2         3         5         7
----------------------------------------------------------------------------------------------

. tabstat pol_interest2 if e(sample), s(n mean sd min p25 p50 p75 max)

    variable |         N      mean        sd       min       p25       p50       p75       max
-------------+--------------------------------------------------------------------------------
pol_intere~2 |       333  3.033033  .7927317         1         3         3         4         4
----------------------------------------------------------------------------------------------

. tabstat ret_level2 if e(sample), s(n mean sd min p25 p50 p75 max)

    variable |         N      mean        sd       min       p25       p50       p75       max
-------------+--------------------------------------------------------------------------------
  ret_level2 |       333  2.351351  .7401805         1         2         2         3         4
----------------------------------------------------------------------------------------------

. 
. 
. estsimp probit vote_leemar ///
>      all_info all_info_D1 ///
>      d1 female inc_level pol_interest2 ret_level2

Iteration 0:   log likelihood = -224.82248
Iteration 1:   log likelihood = -188.81255
Iteration 2:   log likelihood = -188.13752
Iteration 3:   log likelihood = -188.13674

Probit regression                                 Number of obs   =        333
                                                  LR chi2(7)      =      73.37
                                                  Prob > chi2     =     0.0000
Log likelihood = -188.13674                       Pseudo R2       =     0.1632

------------------------------------------------------------------------------
 vote_leemar |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    all_info |  -.1379901   .2359028    -0.58   0.559    -.6003511    .3243709
 all_info_D1 |   .2839149   .2769571     1.03   0.305    -.2589111    .8267409
          d1 |   .4122563    .250017     1.65   0.099    -.0777681    .9022807
      female |  -.3562941   .1510435    -2.36   0.018    -.6523339   -.0602544
   inc_level |   .1077107   .0406876     2.65   0.008     .0279645    .1874569
pol_intere~2 |   .1007426   .0970937     1.04   0.299    -.0895576    .2910427
  ret_level2 |  -.2508242   .1023351    -2.45   0.014    -.4513973    -.050251
       _cons |  -.3923404   .4657303    -0.84   0.400    -1.305155    .5204742
------------------------------------------------------------------------------

Simulating main parameters.  Please wait....
% of simulations completed: 12% 25% 37% 50% 62% 75% 87% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8

. 
. 
. setx median

. setx d1 .448

. setx all_info 0

. setx all_info_D1 0

. simqi, pr listx

You have set the following values for the explanatory variables:

----------------------------------------
     Variable |    Value     Description
--------------+-------------------------
     all_info |         0         0     
  all_info_D1 |         0         0     
           d1 |      .448        .448   
       female |         1       median  
    inc_level |         3       median  
pol_interest2 |         3       median  
   ret_level2 |         2       median  
----------------------------------------


Quantities of interest based on those explanatory values:

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(vote_l~r=0) |   .6698265     .0752529     .5131126    .8014685
            Pr(vote_l~r=1) |   .3301735     .0752529     .1985315    .4868874

. 
. simqi, fd(prval(1) genpr(fd_con)) changex(d1 .017 1.021)

First Difference: d1 .017 1.021

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(vote_l~r = 1) |    .153757     .0870902    -.0101315    .3225016

Simqi generated the following new variable(s): fd_con

. 
. simqi, fd(prval(1) genpr(fd_fem)) changex(female 0 1)

First Difference: female 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(vote_l~r = 1) |  -.1317894     .0551673    -.2367486   -.0260967

Simqi generated the following new variable(s): fd_fem

. simqi, fd(prval(1) genpr(fd_inc)) changex(inc_level 2 5)

First Difference: inc_level 2 5

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(vote_l~r = 1) |   .1164784     .0458311       .02504    .2079138

Simqi generated the following new variable(s): fd_inc

. simqi, fd(prval(1) genpr(fd_int)) changex(pol_interest2 3 4)

First Difference: pol_interest2 3 4

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(vote_l~r = 1) |   .0385904     .0356959    -.0281951    .1146703

Simqi generated the following new variable(s): fd_int

. simqi, fd(prval(1) genpr(fd_ret)) changex(ret_level2 2 3)

First Difference: ret_level2 2 3

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(vote_l~r = 1) |  -.0824352     .0343737    -.1532079   -.0151008

Simqi generated the following new variable(s): fd_ret

. 
. 
. setx d1 0

. simqi, pr listx

You have set the following values for the explanatory variables:

----------------------------------------
     Variable |    Value     Description
--------------+-------------------------
     all_info |         0         0     
  all_info_D1 |         0         0     
           d1 |         0         0     
       female |         1       median  
    inc_level |         3       median  
pol_interest2 |         3       median  
   ret_level2 |         2       median  
----------------------------------------


Quantities of interest based on those explanatory values:

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(vote_l~r=0) |   .7336849     .0800301     .5496767    .8671069
            Pr(vote_l~r=1) |   .2663151     .0800301     .1328932    .4503232

. 
. simqi, fd(prval(1) genpr(tr_all_info_con)) changex(all_info 0 1)

First Difference: all_info 0 1

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(vote_l~r = 1) |  -.0412243     .0783234    -.2118239    .0972884

Simqi generated the following new variable(s): tr_all_info_con

. 
. 
. setx median

. setx all_info 1

. setx all_info_D1 .448

. simqi, pr listx

You have set the following values for the explanatory variables:

----------------------------------------
     Variable |    Value     Description
--------------+-------------------------
     all_info |         1         1     
  all_info_D1 |      .448        .448   
           d1 |      .448       median  
       female |         1       median  
    inc_level |         3       median  
pol_interest2 |         3       median  
   ret_level2 |         2       median  
----------------------------------------


Quantities of interest based on those explanatory values:

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(vote_l~r=0) |   .6726916      .044875     .5794859    .7529904
            Pr(vote_l~r=1) |   .3273084      .044875     .2470096    .4205141

. 
. simqi, fd(prval(1) genpr(fd_all_info)) changex(d1 .017 1.021 all_info_D1 .017 1.021)

First Difference: d1 .017 1.021 all_info_D1 .017 1.021

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
         dPr(vote_l~r = 1) |   .2506709     .0457994     .1604838    .3421449

Simqi generated the following new variable(s): fd_all_info

. 
. 
. setx d1 0

. setx all_info_D1 0

. simqi, pr listx

You have set the following values for the explanatory variables:

----------------------------------------
     Variable |    Value     Description
--------------+-------------------------
     all_info |         1         1     
  all_info_D1 |         0         0     
           d1 |         0         0     
       female |         1       median  
    inc_level |         3       median  
pol_interest2 |         3       median  
   ret_level2 |         2       median  
----------------------------------------


Quantities of interest based on those explanatory values:

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
            Pr(vote_l~r=0) |   .7749092     .0413324     .6827898     .844842
            Pr(vote_l~r=1) |   .2250908     .0413324      .155158    .3172102

. 
. 
. * Difference in baseline probabilities
. 
. tabstat tr_all_info_con, s(mean sd min p5 p10 p50 p90 p95 max)

    variable |      mean        sd       min        p5       p10       p50       p90       p95       max
-------------+------------------------------------------------------------------------------------------
tr_all_inf~n | -.0412243  .0783234 -.3261219 -.1857627 -.1458119 -.0343684  .0506934  .0769118  .1619421
--------------------------------------------------------------------------------------------------------

. 
. 
. * Calculate difference in first differences
. 
. gen dif_all_info_con = fd_all_info - fd_con

. 
. tabstat dif_all_info_con, s(mean sd min p5 p10 p50 p90 p95 max)

    variable |      mean        sd       min        p5       p10       p50       p90       p95       max
-------------+------------------------------------------------------------------------------------------
dif_all_in~n |  .0969139  .0991109 -.1975588 -.0718619 -.0332384  .0992875  .2242197  .2562393  .4251395
--------------------------------------------------------------------------------------------------------

. 
. 
. drop b1-b8

. drop fd_con-dif_all_info_con

. 
. log close
      name:  <unnamed>
       log:  ...\1805 Roadmaps\Table A8 any info demo.log
  log type:  text
 closed on:   8 May 2018, 15:35:40
--------------------------------------------------------------------------------------------------------------------------
